Author_Details
Gordon Linoff and Michael Berry are the founders of Data Miners Inc., a consultancy specializing in data mining. They are the authors of two of the leading data mining titles in the field, both from Wiley: Data Mining Techniques and Mastering Data Mining.
About_Topic
Data mining refers to the analysis of large quantities of data to discover patterns and trends. When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office. Forty thousand copies later, data mining has grown to become an indispensable tool of modern business, used for:
· Targeting and retaining profitable customers
· Avoiding high-risk customers
· Improving response rates for marketing campaigns
· Detecting fraud
Data mining is now taught in most leading business schools--Data Mining Techniques has become a favorite textbook at Wharton, Stanford and other leading business schools.
Government as well uses data mining as a key tool in the war on terrorism. (Data mining is core technology used by the Total Information Awareness Program, for example.) This has led to controversy and higher visibility for this technology. Vendors now offer a host of tools that make data mining simpler and cost-effective--for example, Microsoft's recent releases of SQL Server have made data mining avaailable to a huge new market that had previously been priced out.
About_Book
When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition-more than 50% new and revised- is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company.
Main_Blurb
Short_Blurb
TOC
Introduction.
· What Is Data Mining and Why Do It?
· Data Mining Applications in Marketing and Customer Relationship Management.
· The Data Mining Process.
· Statistics 101: What You Should Know About Data.
· Descriptions and Prediction: Profiling and Predictive Modeling.
· Data Mining Using Classic Statistical Techniques.
· Decision Trees.
· Artifi cial Neural Networks.
· Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering.
· Knowing When to Worry: Using Survival Analysis to Understand Customers.
· Genetic Algorithms and Swarm Intelligence.
· Tell Me Something New: Pattern Discovery and Data Mining.
· Finding Islands of Similarity: Automatic Cluster Detection.
· Alternative Approaches to Cluster Detection.
· Market Basket Analysis and Association Rules.
· Link Analysis.
· Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining.
· Building Customer Signatures.
· Derived Variables: Making the Data Mean More.
· Too Much of a Good Thing? Techniques for Reducing the Number of Variables.
· Listen Carefully to What Your Customers Say: Text Mining.
Index.
Concise_Desc
When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition-more than 50% new and revised- is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company.
· What Is Data Mining and Why Do It?
· Data Mining Applications in Marketing and Customer Relationship Management.
· The Data Mining Process.
· Statistics 101: What You Should Know About Data.
· Descriptions and Prediction: Profiling and Predictive Modeling.
· Data Mining Using Classic Statistical Techniques.
· Decision Trees.
· Artifi cial Neural Networks.
· Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering.
· Knowing When to Worry: Using Survival Analysis to Understand Customers.
· Genetic Algorithms and Swarm Intelligence.
· Tell Me Something New: Pattern Discovery and Data Mining.
· Finding Islands of Similarity: Automatic Cluster Detection.
· Alternative Approaches to Cluster Detection.
· Market Basket Analysis and Association Rules.
· Link Analysis.
· Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining.
· Building Customer Signatures.
· Derived Variables: Making the Data Mean More.
· Too Much of a Good Thing? Techniques for Reducing the Number of Variables.
· Listen Carefully to What Your Customers Say: Text Mining.
